Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we
investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific
case of UDA where a model is adapted to a target domain without access to
source data. We propose a novel approach for the SF-UDA setting based on a loss
reweighting strategy that brings robustness against the noise that inevitably
affects the pseudo-labels. The classification loss is reweighted based on the
reliability of the pseudo-labels that is measured by estimating their
uncertainty. Guided by such reweighting strategy, the pseudo-labels are
progressively refined by aggregating knowledge from neighbouring samples.
Furthermore, a self-supervised contrastive framework is leveraged as a target
space regulariser to enhance such knowledge aggregation. A novel negative pairs
exclusion strategy is proposed to identify and exclude negative pairs made of
samples sharing the same class, even in presence of some noise in the
pseudo-labels. Our method outperforms previous methods on three major
benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C
and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS
with +12.3% in the single-source setting and +6.6% in multi-target adaptation.
Additional analyses demonstrate that the proposed approach is robust to the
noise, which results in significantly more accurate pseudo-labels compared to
state-of-the-art approaches.

本文提出一种基于损失加权策略的 Source-free Unsupervised Domain Adaptation 方法，该方法在没有源数据的情况下，通过估计伪标签的不确定性来逐步优化伪标签，并采用一种自监督对比框架作为目标空间规范化器来增强知识聚合，并在三个基准测试中均取得了显著的优异表现，证明了该方法的鲁棒性。